
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "10"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 79 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 75 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	70.964176	101.816192	inf	inf	19.256931	31.835281	44.927242	75.269637	nan	nan	nan	0.000000	0.000000
2459593	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	265.604300	272.479194	inf	inf	31.785725	76.146817	91.769971	441.806288	nan	nan	nan	0.000000	0.000000
2459592	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	2.988172	-0.249348	6.529591	2.641099	3.521667	-0.317372	4.432545	0.033659	0.0295	0.0303	0.0015	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.817892	5.033615	74.365267	22.634792	36.955301	7.682086	8.510647	0.272534	0.5258	0.6479	0.4172	2.677309	3.881495
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.243321	5.514700	22.086639	19.929361	9.919481	6.858615	2.721187	0.939838	0.6335	0.6465	0.3722	4.717894	4.703083
2459589	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	334.556875	337.531681	inf	inf	18.569911	59.814512	74.931324	438.079150	nan	nan	nan	0.000000	0.000000
2459588	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.875395	4.269777	3.165934	21.686120	4.625585	11.729021	4.246611	12.169122	0.7421	0.7297	0.2234	6.622248	5.883965
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	349.507799	350.941437	inf	inf	-1.598895	104.050622	-4.675000	492.584161	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.636290	2.948349	-0.693779	1.904329	-0.950005	3.557691	-0.070319	0.400694	0.6330	0.6524	0.3719	3.419685	3.473764
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.027681	5.129879	0.309296	3.521668	-0.159020	2.721900	2.891683	1.592645	0.0338	0.0323	0.0011	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.937273	21.949931	2.883699	9.860306	2.559023	22.978820	-0.536526	-0.173327	0.5984	0.6085	0.3746	8.751391	13.476882
2459583	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.382080	4.210671	-0.398523	6.168288	0.231213	6.325889	-0.564126	-0.074573	0.6437	0.6456	0.3943	3.882526	3.639973
2459581	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.798715	6.264872	-0.217399	6.185536	0.108428	3.837699	0.890758	0.536339	0.6329	0.6396	0.4037	3.433596	3.303518
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.389452	4.268787	-0.573695	6.750488	-0.012844	3.717191	-0.544609	0.223225	0.6483	0.6526	0.3895	3.977582	3.654279
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.382301	4.454905	-0.360451	7.850855	-0.103561	5.179499	0.091667	0.788692	0.6432	0.6494	0.3905	3.744772	3.769513
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.533011	-0.883019	-0.529917	-0.041447	0.121189	1.147985	0.036794	-0.162944	0.0313	0.0328	0.0007	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.763717	5.800817	-0.127060	5.725130	0.798746	4.090826	-0.380493	0.506166	0.6440	0.6547	0.3888	3.756091	3.617163
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.648560	4.310802	1.777924	4.638894	1.201615	3.583760	0.612502	1.052211	0.6627	0.6728	0.3886	2.589018	2.580861
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.183873	3.429153	-0.500084	4.358796	0.815280	5.685919	-0.117241	6.179573	0.7152	0.7214	0.3468	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.910983	3.915147	-0.630927	4.331247	0.608120	3.831531	0.395564	0.199205	0.6669	0.6764	0.3774	3.499712	3.272048
2459573	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459572	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.183235	-1.024018	-0.774161	-0.138397	1.484131	0.100275	0.115592	-0.217364	0.0313	0.0346	0.0007	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.164115	3.880177	-0.611554	5.007136	1.082888	5.309744	0.326549	7.568162	0.7132	0.7181	0.2689	3.876473	3.783450
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.942775	4.043216	1.960081	6.068858	0.489267	4.161352	0.760208	0.089510	0.6510	0.6599	0.4032	6.303329	5.028648
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	1.633766	1.633766	1.509412	1.509412	0.566250	0.566250	0.074197	0.074197	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.581344	1.209130	0.143470	-1.974869	-0.918461	-2.158389	-1.388354	-0.580961	0.6608	0.6717	0.3991	2.650618	2.597706
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.378140	4.716232	18.826481	13.828756	9.263191	5.525678	-1.174176	-0.962883	0.6470	0.6595	0.4022	4.358630	4.261997
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.975020	0.501714	-0.040405	-1.529153	-1.191750	-2.415445	-1.175894	-0.833037	0.6552	0.6722	0.3920	2.863035	2.706930
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.578929	1.204475	0.344519	-1.047875	-0.991551	-1.776865	-1.859952	-1.420794	0.6574	0.6745	0.3905	4.603136	4.289099
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.995450	0.773539	-0.043470	-1.205188	-0.960114	-1.678974	-1.371096	-0.937021	0.6575	0.6695	0.4011	0.000000	0.000000
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.803223	1.388746	0.654902	-0.732233	-0.533259	-1.565095	-1.240547	-1.008145	0.6568	0.6740	0.3961	2.787414	2.667196
2459558	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0378	0.0310	0.0010	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.634429	5.384081	11.103216	7.371321	8.708181	4.710235	3.037536	0.356029	0.6637	0.6721	0.3947	2.889697	2.925465
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6524	0.6652	0.4109	nan	nan
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6553	0.6695	0.4067	nan	nan
2459552	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459550	dish_ok	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0586	0.0564	0.0007	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.707462	4.780068	10.605624	7.531134	9.964352	6.223948	-1.051601	-0.868711	0.6516	0.6660	0.4187	2.676100	2.814611
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.174993	5.292622	22.244060	15.518909	0.181800	0.344893	-1.524356	-1.456671	0.7134	0.7131	0.3827	2.639309	2.738641
2459541	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.977620	2.370766	1.745390	0.126313	-0.359062	-1.654121	-1.002714	-1.016575	0.7111	0.6998	0.3752	2.825036	2.664491
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.999330	7.760582	17.815650	12.863559	15.146116	10.773251	-1.751267	-2.396405	0.6847	0.7008	0.4248	3.246513	3.063934
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.489993	1.613289	1.449042	0.021823	-1.777277	-1.750735	-1.916829	-1.107392	0.7431	0.7204	0.3967	2.603940	2.560432
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.750596	1.399519	0.500230	-0.881674	-1.302333	-2.153572	-1.138562	-0.683400	0.7354	0.7213	0.3878	3.288660	3.183921
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.147560	5.577121	11.139187	7.611663	8.682738	6.078626	-1.981204	-1.496637	0.6700	0.6813	0.4086	2.763574	2.857544
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.223078	6.518231	14.618266	9.866887	11.376708	7.585943	-1.615278	-1.567984	0.6703	0.6781	0.4080	3.070508	3.198841
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.692745	1.173129	0.408820	-0.865102	-1.117840	-2.491829	-1.594055	-0.932981	0.6921	0.6964	0.4044	2.822718	2.822520
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.529035	2.770094	5.942928	3.229593	6.376589	10.096165	0.246776	1.334904	0.0394	0.0362	0.0008	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.047584	5.420389	9.853203	5.964002	10.095585	4.948708	9.044091	7.139333	0.8534	0.8805	0.3066	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.950194	5.437252	11.999846	7.170172	3.225947	5.006080	-0.366498	0.116439	0.8540	0.8203	0.2426	5.285063	5.344708

Antenna Season Report Notebook¶

Antenna 10 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 10, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 10: 2459602

Antenna 10: 2459598

Antenna 10: 2459597

Antenna 10: 2459596

Antenna 10: 2459595

Antenna 10: 2459594

Antenna 10: 2459593

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Antenna 10: 2459590

Antenna 10: 2459589

Antenna 10: 2459588

Antenna 10: 2459587

Antenna 10: 2459586

Antenna 10: 2459585

Antenna 10: 2459584

Antenna 10: 2459583

Antenna 10: 2459582

Antenna 10: 2459581

Antenna 10: 2459580

Antenna 10: 2459579

Antenna 10: 2459578

Antenna 10: 2459577

Antenna 10: 2459576

Antenna 10: 2459575

Antenna 10: 2459574

Antenna 10: 2459573

Antenna 10: 2459572

Antenna 10: 2459571

Antenna 10: 2459570

Antenna 10: 2459569

Antenna 10: 2459566

Antenna 10: 2459565

Antenna 10: 2459564

Antenna 10: 2459563

Antenna 10: 2459562

Antenna 10: 2459561

Antenna 10: 2459560

Antenna 10: 2459559

Antenna 10: 2459558

Antenna 10: 2459556

Antenna 10: 2459552

Antenna 10: 2459551

Antenna 10: 2459549

Antenna 10: 2459542

Antenna 10: 2459541

Antenna 10: 2459540

Antenna 10: 2459536

Antenna 10: 2459535

Antenna 10: 2459534

Antenna 10: 2459533

Antenna 10: 2459532

Antenna 10: 2459530

Antenna 10: 2459527

Antenna 10: 2459524

Antenna 10: 2459523

Antenna 10: 2459522

Antenna 10: 2459513

Antenna 10: 2459508

Antenna 10: 2459506

Antenna 10: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶